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About

Sivapong Nilwong is a robotics researcher whose work sits at the intersection of deep learning, computer vision, and autonomous navigation. His primary research areas include outdoor mobile robot localization, deep reinforcement learning for navigation, and object recognition under challenging conditions. Nilwong’s most impactful contribution is his 2019 paper on deep learning-based landmark detection for outdoor robot localization (51 citations), which addresses the critical limitation of GPS accuracy in varying environmental conditions. He further advanced the field by developing game-based Deep Q-Network (DQN) methods for outdoor robot navigation, integrating augmented reality to train navigation policies in simulated first-person environments. His work extends to practical applications, including a Faster R-CNN approach for recognizing partially occluded objects (3 citations) and a low-cost myoelectric robotic hand developed using 3D printing technology (3 citations). Most recently, Nilwong has explored vision-based food recognition systems for autonomous cooking robots, specifically for Japanese yakiniku. His research demonstrates a consistent focus on bridging simulation-to-reality gaps and creating robust, cost-effective solutions for real-world robotic systems.